Overview

Dataset statistics

Number of variables16
Number of observations13373
Missing cells39355
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory466.4 B

Variable types

Text3
Categorical3
Numeric10

Alerts

Year_of_Release has 216 (1.6%) missing valuesMissing
Critic_Score has 6874 (51.4%) missing valuesMissing
Critic_Count has 6874 (51.4%) missing valuesMissing
User_Score has 7310 (54.7%) missing valuesMissing
User_Count has 7310 (54.7%) missing valuesMissing
Developer has 5304 (39.7%) missing valuesMissing
Rating has 5422 (40.5%) missing valuesMissing
Other_Sales is highly skewed (γ1 = 20.06304498)Skewed
NA_Sales has 3636 (27.2%) zerosZeros
EU_Sales has 4704 (35.2%) zerosZeros
JP_Sales has 8391 (62.7%) zerosZeros
Other_Sales has 5303 (39.7%) zerosZeros

Reproduction

Analysis started2024-06-24 13:07:03.502756
Analysis finished2024-06-24 13:07:07.842638
Duration4.34 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Name
Text

Distinct9754
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-06-24T15:07:08.028950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length132
Median length85
Mean length23.966574
Min length1

Characters and Unicode

Total characters320505
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7590 ?
Unique (%)56.8%

Sample

1st rowWorld Series of Poker 2008: Battle for the Bracelets
2nd rowGermany's Next Topmodel 2011
3rd rowAssassin's Creed: Unity
4th rowTetris Attack
5th rowMen of War
ValueCountFrequency (%)
the 2196
 
4.1%
of 1385
 
2.6%
2 948
 
1.8%
no 611
 
1.2%
602
 
1.1%
3 432
 
0.8%
world 317
 
0.6%
pro 254
 
0.5%
game 247
 
0.5%
ii 247
 
0.5%
Other values (8448) 45738
86.3%
2024-06-24T15:07:08.300179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39629
 
12.4%
e 25839
 
8.1%
a 21504
 
6.7%
o 19393
 
6.1%
r 16980
 
5.3%
i 16770
 
5.2%
n 16471
 
5.1%
t 13826
 
4.3%
s 12519
 
3.9%
l 9916
 
3.1%
Other values (85) 127658
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
39629
 
12.4%
e 25839
 
8.1%
a 21504
 
6.7%
o 19393
 
6.1%
r 16980
 
5.3%
i 16770
 
5.2%
n 16471
 
5.1%
t 13826
 
4.3%
s 12519
 
3.9%
l 9916
 
3.1%
Other values (85) 127658
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
39629
 
12.4%
e 25839
 
8.1%
a 21504
 
6.7%
o 19393
 
6.1%
r 16980
 
5.3%
i 16770
 
5.2%
n 16471
 
5.1%
t 13826
 
4.3%
s 12519
 
3.9%
l 9916
 
3.1%
Other values (85) 127658
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
39629
 
12.4%
e 25839
 
8.1%
a 21504
 
6.7%
o 19393
 
6.1%
r 16980
 
5.3%
i 16770
 
5.2%
n 16471
 
5.1%
t 13826
 
4.3%
s 12519
 
3.9%
l 9916
 
3.1%
Other values (85) 127658
39.8%

Platform
Categorical

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size885.1 KiB
PS2
1745 
DS
1705 
PS3
1085 
Wii
1053 
X360
1006 
Other values (25)
6779 

Length

Max length4
Median length3
Mean length2.7720033
Min length2

Characters and Unicode

Total characters37070
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPS3
2nd rowDS
3rd rowPC
4th rowSNES
5th rowPC

Common Values

ValueCountFrequency (%)
PS2 1745
13.0%
DS 1705
12.7%
PS3 1085
 
8.1%
Wii 1053
 
7.9%
X360 1006
 
7.5%
PSP 971
 
7.3%
PS 946
 
7.1%
PC 770
 
5.8%
XB 661
 
4.9%
GBA 658
 
4.9%
Other values (20) 2773
20.7%

Length

2024-06-24T15:07:08.360187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ps2 1745
13.0%
ds 1705
12.7%
ps3 1085
 
8.1%
wii 1053
 
7.9%
x360 1006
 
7.5%
psp 971
 
7.3%
ps 946
 
7.1%
pc 770
 
5.8%
xb 661
 
4.9%
gba 658
 
4.9%
Other values (20) 2773
20.7%

Most occurring characters

ValueCountFrequency (%)
S 8140
22.0%
P 7169
19.3%
3 2506
 
6.8%
i 2346
 
6.3%
D 2168
 
5.8%
X 1856
 
5.0%
2 1850
 
5.0%
B 1396
 
3.8%
6 1373
 
3.7%
C 1251
 
3.4%
Other values (15) 7015
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 8140
22.0%
P 7169
19.3%
3 2506
 
6.8%
i 2346
 
6.3%
D 2168
 
5.8%
X 1856
 
5.0%
2 1850
 
5.0%
B 1396
 
3.8%
6 1373
 
3.7%
C 1251
 
3.4%
Other values (15) 7015
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 8140
22.0%
P 7169
19.3%
3 2506
 
6.8%
i 2346
 
6.3%
D 2168
 
5.8%
X 1856
 
5.0%
2 1850
 
5.0%
B 1396
 
3.8%
6 1373
 
3.7%
C 1251
 
3.4%
Other values (15) 7015
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 8140
22.0%
P 7169
19.3%
3 2506
 
6.8%
i 2346
 
6.3%
D 2168
 
5.8%
X 1856
 
5.0%
2 1850
 
5.0%
B 1396
 
3.8%
6 1373
 
3.7%
C 1251
 
3.4%
Other values (15) 7015
18.9%

Year_of_Release
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)0.3%
Missing216
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2006.5074
Minimum1980
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:08.396704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1996
Q12003
median2007
Q32010
95-th percentile2015
Maximum2020
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.8802309
Coefficient of variation (CV)0.0029305802
Kurtosis1.7594068
Mean2006.5074
Median Absolute Deviation (MAD)4
Skewness-0.9716569
Sum26399618
Variance34.577115
MonotonicityNot monotonic
2024-06-24T15:07:08.433876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2009 1119
 
8.4%
2008 1113
 
8.3%
2010 1018
 
7.6%
2011 940
 
7.0%
2007 938
 
7.0%
2006 805
 
6.0%
2005 757
 
5.7%
2002 653
 
4.9%
2003 630
 
4.7%
2004 615
 
4.6%
Other values (29) 4569
34.2%
ValueCountFrequency (%)
1980 6
 
< 0.1%
1981 39
0.3%
1982 26
0.2%
1983 15
 
0.1%
1984 11
 
0.1%
1985 12
 
0.1%
1986 17
0.1%
1987 10
 
0.1%
1988 14
 
0.1%
1989 15
 
0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2017 3
 
< 0.1%
2016 406
 
3.0%
2015 479
3.6%
2014 469
3.5%
2013 435
 
3.3%
2012 537
4.0%
2011 940
7.0%
2010 1018
7.6%
2009 1119
8.4%

Genre
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size942.1 KiB
Action
2696 
Sports
1878 
Misc
1400 
Role-Playing
1200 
Shooter
1058 
Other values (7)
5141 

Length

Max length12
Median length10
Mean length7.1406565
Min length4

Characters and Unicode

Total characters95492
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMisc
2nd rowSimulation
3rd rowAction
4th rowPuzzle
5th rowStrategy

Common Values

ValueCountFrequency (%)
Action 2696
20.2%
Sports 1878
14.0%
Misc 1400
10.5%
Role-Playing 1200
9.0%
Shooter 1058
 
7.9%
Adventure 1042
 
7.8%
Racing 999
 
7.5%
Platform 711
 
5.3%
Simulation 699
 
5.2%
Fighting 679
 
5.1%
Other values (2) 1011
 
7.6%

Length

2024-06-24T15:07:08.472697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action 2696
20.2%
sports 1878
14.0%
misc 1400
10.5%
role-playing 1200
9.0%
shooter 1058
 
7.9%
adventure 1042
 
7.8%
racing 999
 
7.5%
platform 711
 
5.3%
simulation 699
 
5.2%
fighting 679
 
5.1%
Other values (2) 1011
 
7.6%

Most occurring characters

ValueCountFrequency (%)
t 9857
 
10.3%
o 9300
 
9.7%
i 9051
 
9.5%
n 7315
 
7.7%
e 5353
 
5.6%
r 5236
 
5.5%
c 5095
 
5.3%
l 4274
 
4.5%
S 4182
 
4.4%
a 4156
 
4.4%
Other values (17) 31673
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 9857
 
10.3%
o 9300
 
9.7%
i 9051
 
9.5%
n 7315
 
7.7%
e 5353
 
5.6%
r 5236
 
5.5%
c 5095
 
5.3%
l 4274
 
4.5%
S 4182
 
4.4%
a 4156
 
4.4%
Other values (17) 31673
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 9857
 
10.3%
o 9300
 
9.7%
i 9051
 
9.5%
n 7315
 
7.7%
e 5353
 
5.6%
r 5236
 
5.5%
c 5095
 
5.3%
l 4274
 
4.5%
S 4182
 
4.4%
a 4156
 
4.4%
Other values (17) 31673
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 9857
 
10.3%
o 9300
 
9.7%
i 9051
 
9.5%
n 7315
 
7.7%
e 5353
 
5.6%
r 5236
 
5.5%
c 5095
 
5.3%
l 4274
 
4.5%
S 4182
 
4.4%
a 4156
 
4.4%
Other values (17) 31673
33.2%
Distinct548
Distinct (%)4.1%
Missing45
Missing (%)0.3%
Memory size1.0 MiB
2024-06-24T15:07:08.672029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length38
Median length28
Mean length13.606993
Min length3

Characters and Unicode

Total characters181354
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)1.4%

Sample

1st rowActivision
2nd row7G//AMES
3rd rowUbisoft
4th rowNintendo
5th row505 Games
ValueCountFrequency (%)
entertainment 1987
 
8.2%
games 1600
 
6.6%
interactive 1298
 
5.3%
arts 1078
 
4.4%
electronic 1077
 
4.4%
activision 802
 
3.3%
namco 764
 
3.1%
bandai 764
 
3.1%
ubisoft 758
 
3.1%
digital 745
 
3.1%
Other values (613) 13440
55.3%
2024-06-24T15:07:08.940850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 17118
 
9.4%
e 15973
 
8.8%
i 15459
 
8.5%
n 15053
 
8.3%
a 13711
 
7.6%
o 11222
 
6.2%
10985
 
6.1%
r 9612
 
5.3%
s 7374
 
4.1%
m 7304
 
4.0%
Other values (59) 57543
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 17118
 
9.4%
e 15973
 
8.8%
i 15459
 
8.5%
n 15053
 
8.3%
a 13711
 
7.6%
o 11222
 
6.2%
10985
 
6.1%
r 9612
 
5.3%
s 7374
 
4.1%
m 7304
 
4.0%
Other values (59) 57543
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 17118
 
9.4%
e 15973
 
8.8%
i 15459
 
8.5%
n 15053
 
8.3%
a 13711
 
7.6%
o 11222
 
6.2%
10985
 
6.1%
r 9612
 
5.3%
s 7374
 
4.1%
m 7304
 
4.0%
Other values (59) 57543
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 17118
 
9.4%
e 15973
 
8.8%
i 15459
 
8.5%
n 15053
 
8.3%
a 13711
 
7.6%
o 11222
 
6.2%
10985
 
6.1%
r 9612
 
5.3%
s 7374
 
4.1%
m 7304
 
4.0%
Other values (59) 57543
31.7%

NA_Sales
Real number (ℝ)

ZEROS 

Distinct375
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26551484
Minimum0
Maximum41.36
Zeros3636
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.003691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08
Q30.24
95-th percentile1.06
Maximum41.36
Range41.36
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.84922944
Coefficient of variation (CV)3.1984255
Kurtosis668.55795
Mean0.26551484
Median Absolute Deviation (MAD)0.08
Skewness19.506166
Sum3550.73
Variance0.72119064
MonotonicityNot monotonic
2024-06-24T15:07:09.041787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3636
27.2%
0.03 459
 
3.4%
0.02 452
 
3.4%
0.01 433
 
3.2%
0.04 431
 
3.2%
0.05 425
 
3.2%
0.06 403
 
3.0%
0.07 385
 
2.9%
0.08 365
 
2.7%
0.09 345
 
2.6%
Other values (365) 6039
45.2%
ValueCountFrequency (%)
0 3636
27.2%
0.01 433
 
3.2%
0.02 452
 
3.4%
0.03 459
 
3.4%
0.04 431
 
3.2%
0.05 425
 
3.2%
0.06 403
 
3.0%
0.07 385
 
2.9%
0.08 365
 
2.7%
0.09 345
 
2.6%
ValueCountFrequency (%)
41.36 1
< 0.1%
29.08 1
< 0.1%
26.93 1
< 0.1%
23.2 1
< 0.1%
15.68 1
< 0.1%
15 1
< 0.1%
14.44 1
< 0.1%
13.96 1
< 0.1%
11.28 1
< 0.1%
11.27 1
< 0.1%

EU_Sales
Real number (ℝ)

ZEROS 

Distinct284
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14706349
Minimum0
Maximum28.96
Zeros4704
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.080142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.11
95-th percentile0.63
Maximum28.96
Range28.96
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.52376058
Coefficient of variation (CV)3.561459
Kurtosis787.42954
Mean0.14706349
Median Absolute Deviation (MAD)0.02
Skewness19.641648
Sum1966.68
Variance0.27432514
MonotonicityNot monotonic
2024-06-24T15:07:09.118294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4704
35.2%
0.01 1187
 
8.9%
0.02 1061
 
7.9%
0.03 741
 
5.5%
0.04 562
 
4.2%
0.05 460
 
3.4%
0.06 315
 
2.4%
0.07 284
 
2.1%
0.08 257
 
1.9%
0.09 221
 
1.7%
Other values (274) 3581
26.8%
ValueCountFrequency (%)
0 4704
35.2%
0.01 1187
 
8.9%
0.02 1061
 
7.9%
0.03 741
 
5.5%
0.04 562
 
4.2%
0.05 460
 
3.4%
0.06 315
 
2.4%
0.07 284
 
2.1%
0.08 257
 
1.9%
0.09 221
 
1.7%
ValueCountFrequency (%)
28.96 1
< 0.1%
12.76 1
< 0.1%
10.95 1
< 0.1%
9.2 1
< 0.1%
9.18 1
< 0.1%
9.14 1
< 0.1%
9.09 1
< 0.1%
8.89 1
< 0.1%
8.49 1
< 0.1%
8.03 1
< 0.1%

JP_Sales
Real number (ℝ)

ZEROS 

Distinct219
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.076706797
Minimum0
Maximum10.22
Zeros8391
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.158352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.36
Maximum10.22
Range10.22
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.30777663
Coefficient of variation (CV)4.0123776
Kurtosis223.57664
Mean0.076706797
Median Absolute Deviation (MAD)0
Skewness12.021363
Sum1025.8
Variance0.094726456
MonotonicityNot monotonic
2024-06-24T15:07:09.198179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8391
62.7%
0.02 583
 
4.4%
0.01 558
 
4.2%
0.03 437
 
3.3%
0.04 324
 
2.4%
0.05 256
 
1.9%
0.06 247
 
1.8%
0.07 182
 
1.4%
0.08 175
 
1.3%
0.1 124
 
0.9%
Other values (209) 2096
 
15.7%
ValueCountFrequency (%)
0 8391
62.7%
0.01 558
 
4.2%
0.02 583
 
4.4%
0.03 437
 
3.3%
0.04 324
 
2.4%
0.05 256
 
1.9%
0.06 247
 
1.8%
0.07 182
 
1.4%
0.08 175
 
1.3%
0.09 121
 
0.9%
ValueCountFrequency (%)
10.22 1
< 0.1%
7.2 1
< 0.1%
6.81 1
< 0.1%
6.5 1
< 0.1%
6.04 1
< 0.1%
5.65 1
< 0.1%
5.38 1
< 0.1%
5.33 1
< 0.1%
4.87 1
< 0.1%
4.7 1
< 0.1%

Other_Sales
Real number (ℝ)

SKEWED  ZEROS 

Distinct139
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047124056
Minimum0
Maximum8.45
Zeros5303
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.237666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.03
95-th percentile0.2
Maximum8.45
Range8.45
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.17452221
Coefficient of variation (CV)3.7034632
Kurtosis728.22213
Mean0.047124056
Median Absolute Deviation (MAD)0.01
Skewness20.063045
Sum630.19
Variance0.030458001
MonotonicityNot monotonic
2024-06-24T15:07:09.275372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5303
39.7%
0.01 2774
20.7%
0.02 1274
 
9.5%
0.03 731
 
5.5%
0.04 525
 
3.9%
0.05 373
 
2.8%
0.06 305
 
2.3%
0.07 279
 
2.1%
0.08 198
 
1.5%
0.09 148
 
1.1%
Other values (129) 1463
 
10.9%
ValueCountFrequency (%)
0 5303
39.7%
0.01 2774
20.7%
0.02 1274
 
9.5%
0.03 731
 
5.5%
0.04 525
 
3.9%
0.05 373
 
2.8%
0.06 305
 
2.3%
0.07 279
 
2.1%
0.08 198
 
1.5%
0.09 148
 
1.1%
ValueCountFrequency (%)
8.45 1
< 0.1%
7.53 1
< 0.1%
3.96 1
< 0.1%
3.29 1
< 0.1%
2.93 1
< 0.1%
2.88 1
< 0.1%
2.84 1
< 0.1%
2.74 1
< 0.1%
2.46 1
< 0.1%
2.38 1
< 0.1%

Global_Sales
Real number (ℝ)

Distinct576
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5366567
Minimum0.01
Maximum82.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.314051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.06
median0.17
Q30.47
95-th percentile2.044
Maximum82.53
Range82.52
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation1.6022149
Coefficient of variation (CV)2.985549
Kurtosis639.11477
Mean0.5366567
Median Absolute Deviation (MAD)0.14
Skewness18.202614
Sum7176.71
Variance2.5670924
MonotonicityNot monotonic
2024-06-24T15:07:09.351517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 860
 
6.4%
0.03 715
 
5.3%
0.01 514
 
3.8%
0.04 513
 
3.8%
0.05 495
 
3.7%
0.06 472
 
3.5%
0.07 405
 
3.0%
0.08 393
 
2.9%
0.11 353
 
2.6%
0.09 350
 
2.6%
Other values (566) 8303
62.1%
ValueCountFrequency (%)
0.01 514
3.8%
0.02 860
6.4%
0.03 715
5.3%
0.04 513
3.8%
0.05 495
3.7%
0.06 472
3.5%
0.07 405
3.0%
0.08 393
2.9%
0.09 350
2.6%
0.1 331
 
2.5%
ValueCountFrequency (%)
82.53 1
< 0.1%
40.24 1
< 0.1%
35.52 1
< 0.1%
31.37 1
< 0.1%
30.26 1
< 0.1%
29.8 1
< 0.1%
28.92 1
< 0.1%
28.32 1
< 0.1%
28.31 1
< 0.1%
24.67 1
< 0.1%

Critic_Score
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)1.3%
Missing6874
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean68.962148
Minimum13
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.390329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile43
Q160
median71
Q379
95-th percentile89
Maximum98
Range85
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.960276
Coefficient of variation (CV)0.20243389
Kurtosis0.17494308
Mean68.962148
Median Absolute Deviation (MAD)9
Skewness-0.62591982
Sum448185
Variance194.8893
MonotonicityNot monotonic
2024-06-24T15:07:09.427631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 207
 
1.5%
71 198
 
1.5%
75 196
 
1.5%
73 191
 
1.4%
76 189
 
1.4%
77 188
 
1.4%
80 187
 
1.4%
78 187
 
1.4%
72 184
 
1.4%
79 180
 
1.3%
Other values (72) 4592
34.3%
(Missing) 6874
51.4%
ValueCountFrequency (%)
13 1
 
< 0.1%
17 1
 
< 0.1%
19 5
< 0.1%
20 3
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 3
 
< 0.1%
24 4
< 0.1%
25 6
< 0.1%
26 8
0.1%
ValueCountFrequency (%)
98 4
 
< 0.1%
97 10
 
0.1%
96 15
 
0.1%
95 11
 
0.1%
94 31
 
0.2%
93 35
0.3%
92 42
0.3%
91 50
0.4%
90 63
0.5%
89 87
0.7%

Critic_Count
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)1.6%
Missing6874
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean26.417757
Minimum3
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.466407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q111
median21
Q336
95-th percentile65.1
Maximum113
Range110
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.122553
Coefficient of variation (CV)0.72385226
Kurtosis0.99765562
Mean26.417757
Median Absolute Deviation (MAD)11
Skewness1.1507173
Sum171689
Variance365.67202
MonotonicityNot monotonic
2024-06-24T15:07:09.504493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 246
 
1.8%
5 221
 
1.7%
9 206
 
1.5%
11 205
 
1.5%
8 201
 
1.5%
7 200
 
1.5%
17 195
 
1.5%
6 186
 
1.4%
12 178
 
1.3%
16 174
 
1.3%
Other values (95) 4487
33.6%
(Missing) 6874
51.4%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 246
1.8%
5 221
1.7%
6 186
1.4%
7 200
1.5%
8 201
1.5%
9 206
1.5%
10 162
1.2%
11 205
1.5%
12 178
1.3%
ValueCountFrequency (%)
113 1
 
< 0.1%
107 1
 
< 0.1%
106 1
 
< 0.1%
105 1
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 3
< 0.1%
99 1
 
< 0.1%

User_Score
Real number (ℝ)

MISSING 

Distinct95
Distinct (%)1.6%
Missing7310
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean7.1214745
Minimum0
Maximum9.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.543930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.1
Q16.4
median7.5
Q38.2
95-th percentile8.9
Maximum9.7
Range9.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.505261
Coefficient of variation (CV)0.21136929
Kurtosis1.8394894
Mean7.1214745
Median Absolute Deviation (MAD)0.8
Skewness-1.2797237
Sum43177.5
Variance2.2658106
MonotonicityNot monotonic
2024-06-24T15:07:09.582575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 262
 
2.0%
8.2 234
 
1.7%
8 232
 
1.7%
8.5 207
 
1.5%
7.5 203
 
1.5%
8.3 197
 
1.5%
7.9 197
 
1.5%
8.1 194
 
1.5%
7.3 191
 
1.4%
8.4 184
 
1.4%
Other values (85) 3962
29.6%
(Missing) 7310
54.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.2 2
< 0.1%
0.3 2
< 0.1%
0.5 1
 
< 0.1%
0.6 2
< 0.1%
0.7 2
< 0.1%
0.9 2
< 0.1%
1 2
< 0.1%
1.1 2
< 0.1%
1.2 3
< 0.1%
ValueCountFrequency (%)
9.7 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 5
 
< 0.1%
9.4 10
 
0.1%
9.3 20
 
0.1%
9.2 36
 
0.3%
9.1 72
0.5%
9 91
0.7%
8.9 115
0.9%
8.8 147
1.1%

User_Count
Real number (ℝ)

MISSING 

Distinct798
Distinct (%)13.2%
Missing7310
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean164.98582
Minimum4
Maximum10665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-24T15:07:09.621343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q110
median24
Q382
95-th percentile767.9
Maximum10665
Range10661
Interquartile range (IQR)72

Descriptive statistics

Standard deviation576.10243
Coefficient of variation (CV)3.4918301
Kurtosis114.6422
Mean164.98582
Median Absolute Deviation (MAD)18
Skewness9.1768042
Sum1000309
Variance331894.01
MonotonicityNot monotonic
2024-06-24T15:07:09.661229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 277
 
2.1%
4 273
 
2.0%
5 257
 
1.9%
8 232
 
1.7%
7 222
 
1.7%
9 185
 
1.4%
11 148
 
1.1%
10 144
 
1.1%
12 134
 
1.0%
13 134
 
1.0%
Other values (788) 4057
30.3%
(Missing) 7310
54.7%
ValueCountFrequency (%)
4 273
2.0%
5 257
1.9%
6 277
2.1%
7 222
1.7%
8 232
1.7%
9 185
1.4%
10 144
1.1%
11 148
1.1%
12 134
1.0%
13 134
1.0%
ValueCountFrequency (%)
10665 1
< 0.1%
10179 1
< 0.1%
9851 1
< 0.1%
9629 1
< 0.1%
9073 1
< 0.1%
8713 1
< 0.1%
8003 1
< 0.1%
7512 1
< 0.1%
7322 1
< 0.1%
7064 1
< 0.1%

Developer
Text

MISSING 

Distinct1556
Distinct (%)19.3%
Missing5304
Missing (%)39.7%
Memory size824.3 KiB
2024-06-24T15:07:09.844938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length80
Median length47
Mean length13.318875
Min length2

Characters and Unicode

Total characters107470
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique633 ?
Unique (%)7.8%

Sample

1st rowLeft Field Productions
2nd rowUbisoft, Ubisoft Montreal
3rd rowBest Way
4th rowPyramid
5th rowAtlus
ValueCountFrequency (%)
games 880
 
5.6%
studios 623
 
3.9%
ea 501
 
3.2%
entertainment 500
 
3.2%
software 411
 
2.6%
ubisoft 373
 
2.4%
interactive 288
 
1.8%
sports 194
 
1.2%
inc 150
 
1.0%
canada 140
 
0.9%
Other values (1544) 11726
74.3%
2024-06-24T15:07:10.194895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8829
 
8.2%
a 8145
 
7.6%
7717
 
7.2%
t 7530
 
7.0%
i 7436
 
6.9%
o 7144
 
6.6%
n 6154
 
5.7%
r 5359
 
5.0%
s 5322
 
5.0%
m 3485
 
3.2%
Other values (65) 40349
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8829
 
8.2%
a 8145
 
7.6%
7717
 
7.2%
t 7530
 
7.0%
i 7436
 
6.9%
o 7144
 
6.6%
n 6154
 
5.7%
r 5359
 
5.0%
s 5322
 
5.0%
m 3485
 
3.2%
Other values (65) 40349
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8829
 
8.2%
a 8145
 
7.6%
7717
 
7.2%
t 7530
 
7.0%
i 7436
 
6.9%
o 7144
 
6.6%
n 6154
 
5.7%
r 5359
 
5.0%
s 5322
 
5.0%
m 3485
 
3.2%
Other values (65) 40349
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8829
 
8.2%
a 8145
 
7.6%
7717
 
7.2%
t 7530
 
7.0%
i 7436
 
6.9%
o 7144
 
6.6%
n 6154
 
5.7%
r 5359
 
5.0%
s 5322
 
5.0%
m 3485
 
3.2%
Other values (65) 40349
37.5%

Rating
Categorical

MISSING 

Distinct8
Distinct (%)0.1%
Missing5422
Missing (%)40.5%
Memory size854.7 KiB
E
3179 
T
2378 
M
1234 
E10+
1149 
EC
 
5
Other values (3)
 
6

Length

Max length4
Median length1
Mean length1.4351654
Min length1

Characters and Unicode

Total characters11411
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowT
2nd rowM
3rd rowM
4th rowT
5th rowT

Common Values

ValueCountFrequency (%)
E 3179
23.8%
T 2378
17.8%
M 1234
 
9.2%
E10+ 1149
 
8.6%
EC 5
 
< 0.1%
RP 3
 
< 0.1%
K-A 2
 
< 0.1%
AO 1
 
< 0.1%
(Missing) 5422
40.5%

Length

2024-06-24T15:07:10.252866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-24T15:07:10.292420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
e 3179
40.0%
t 2378
29.9%
m 1234
 
15.5%
e10 1149
 
14.5%
ec 5
 
0.1%
rp 3
 
< 0.1%
k-a 2
 
< 0.1%
ao 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 4333
38.0%
T 2378
20.8%
M 1234
 
10.8%
1 1149
 
10.1%
0 1149
 
10.1%
+ 1149
 
10.1%
C 5
 
< 0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
A 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4333
38.0%
T 2378
20.8%
M 1234
 
10.8%
1 1149
 
10.1%
0 1149
 
10.1%
+ 1149
 
10.1%
C 5
 
< 0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
A 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4333
38.0%
T 2378
20.8%
M 1234
 
10.8%
1 1149
 
10.1%
0 1149
 
10.1%
+ 1149
 
10.1%
C 5
 
< 0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
A 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4333
38.0%
T 2378
20.8%
M 1234
 
10.8%
1 1149
 
10.1%
0 1149
 
10.1%
+ 1149
 
10.1%
C 5
 
< 0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
A 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Interactions

2024-06-24T15:07:07.185871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:03.766865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.371377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.723705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.095566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.483722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.812562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.138352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.529068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.859109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.221216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:03.861985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.413750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.765643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.132900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.520957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.849052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.172917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.564215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.893944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.253437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:03.929195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.448850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.802006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.165280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.552453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.880421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.204533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.596494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.925835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.287617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.000295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.487209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.841352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.200009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.585906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.914675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.237889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.630692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.960103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.321682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.065085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.524614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.877837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.286585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.620462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.946705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.270632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.664775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.993831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.353913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.113974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.557781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.912204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.319329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.652079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.978327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.302387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.696797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.025744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.386008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.153888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.590561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.945739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.351032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.683891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.009812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.399873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.728864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.056777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.418311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.195286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.623459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.979071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.383265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.715555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.041649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.431737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.760898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.088269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.453119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.287137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.656554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.018256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.416360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.747452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.073895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.464503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.793976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.120601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.490389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.331110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:04.689742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.060576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.449742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:05.779526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.105687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.496257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:06.826229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-24T15:07:07.153245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Missing values

2024-06-24T15:07:07.553741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-24T15:07:07.636987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-24T15:07:07.725614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRating
10676World Series of Poker 2008: Battle for the BraceletsPS32007.0MiscActivision0.080.010.000.010.1075.06.0NaNNaNLeft Field ProductionsT
16066Germany's Next Topmodel 2011DS2011.0Simulation7G//AMES0.000.010.000.000.02NaNNaNNaNNaNNaNNaN
3652Assassin's Creed: UnityPC2014.0ActionUbisoft0.180.330.000.040.5570.010.03.01463.0Ubisoft, Ubisoft MontrealM
8863Tetris AttackSNES1995.0PuzzleNintendo0.000.000.150.000.15NaNNaNNaNNaNNaNNaN
14449Men of WarPC2009.0Strategy505 Games0.010.020.000.000.0380.022.08.1136.0Best WayM
6255Dragon Ball Z: Budokai 2GC2004.0FightingAtari0.210.060.000.010.2766.09.05.812.0PyramidT
278FIFA 12X3602011.0SportsElectronic Arts0.842.780.020.534.18NaNNaNNaNNaNNaNNaN
3659Secret Agent Clank(US sales)PSP2008.0PlatformSony Computer Entertainment0.330.220.000.000.55NaNNaNNaNNaNNaNNaN
6443Shin Megami Tensei: Devil SurvivorDS2009.0Role-PlayingAtlus0.130.000.120.010.2684.028.08.879.0AtlusT
14003DT CarnagePSP2009.0RacingAgetec0.030.000.000.000.04NaNNaNNaNNaNAxis EntertainmentE10+
NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRating
4779Gauntlet: Seven SorrowsPS22005.0Role-PlayingMidway Games0.200.150.000.050.4059.038.05.720.0MidwayT
5373Thunderstrike: Operation PhoenixPS22001.0SimulationEidos Interactive0.170.130.000.040.3465.015.08.17.0Core Design Ltd.T
11110Kenka Banchou 2: Full ThrottlePS22007.0ActionSpike0.000.000.090.000.09NaNNaNNaNNaNNaNNaN
12951Bust-A-BlocPS22002.0PuzzleMidas Interactive Entertainment0.000.000.050.000.05NaNNaNNaNNaNNaNNaN
8222Ridge RacerPSV2011.0RacingNamco Bandai Games0.030.070.050.020.1744.039.03.759.0Namco Bandai Games, CelliusE10+
1333NCAA Football 2003PS22002.0SportsElectronic Arts1.160.080.000.191.4491.019.08.627.0EA SportsE
12033SNK Arcade Classics Vol. 1Wii2008.0MiscIgnition Entertainment0.060.000.000.000.0773.018.08.04.0SNK PlaymoreT
16321Thunder AlleyGBA2004.0RacingXS Games0.010.000.000.000.0123.04.0NaNNaNPronto GamesE
1838Guitar Hero Encore: Rocks The 80sPS22007.0MiscRedOctane0.920.040.000.151.1169.050.06.331.0Harmonix Music SystemsT
4039Resident Evil: The Mercenaries 3D3DS2011.0ActionCapcom0.160.160.130.030.4965.069.06.4110.0CapcomM